SOTAVerified

Data Augmentation

Data augmentation involves techniques used for increasing the amount of data, based on different modifications, to expand the amount of examples in the original dataset. Data augmentation not only helps to grow the dataset but it also increases the diversity of the dataset. When training machine learning models, data augmentation acts as a regularizer and helps to avoid overfitting.

Data augmentation techniques have been found useful in domains like NLP and computer vision. In computer vision, transformations like cropping, flipping, and rotation are used. In NLP, data augmentation techniques can include swapping, deletion, random insertion, among others.

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Papers

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Showing 6301–6350 of 8378 papers

TitleStatusHype
AUGVIC: Exploiting BiText Vicinity for Low-Resource NMT—0
Joint System-Wise Optimization for Pipeline Goal-Oriented Dialog System—0
Offline Inverse Reinforcement Learning—0
A multi-stage GAN for multi-organ chest X-ray image generation and segmentation—0
Tensor feature hallucination for few-shot learningCode0
Generative adversarial network with object detector discriminator for enhanced defect detection on ultrasonic B-scans—0
Cheap and Good? Simple and Effective Data Augmentation for Low Resource Machine ReadingCode0
Theoretically Motivated Data Augmentation and Regularization for Portfolio ConstructionCode0
Data Augmentation Methods for End-to-end Speech Recognition on Distant-Talk Scenarios—0
EventDrop: data augmentation for event-based learningCode0
Rotating spiders and reflecting dogs: a class conditional approach to learning data augmentation distributions—0
On the Language Coverage Bias for Neural Machine Translation—0
Exploiting Language Relatedness for Low Web-Resource Language Model Adaptation: An Indic Languages StudyCode0
RegMix: Data Mixing Augmentation for Regression—0
Feature-based Style Randomization for Domain Generalization—0
Go with the Flows: Mixtures of Normalizing Flows for Point Cloud Generation and Reconstruction—0
Training Robust Graph Neural Networks with Topology Adaptive Edge Dropping—0
AOSLO-net: A deep learning-based method for automatic segmentation of retinal microaneurysms from adaptive optics scanning laser ophthalmoscope images—0
Cross-language Sentence Selection via Data Augmentation and Rationale Training—0
Bi-Granularity Contrastive Learning for Post-Training in Few-Shot Scene—0
Can vectors read minds better than experts? Comparing data augmentation strategies for the automated scoring of children's mindreading ability—0
Finding and Fixing Spurious Patterns with Explanations—0
LearnDA: Learnable Knowledge-Guided Data Augmentation for Event Causality Identification—0
Pathology-Aware Generative Adversarial Networks for Medical Image Augmentation—0
Bayesian Inference for Gamma Models—0
Noisy student-teacher training for robust keyword spotting—0
Speaker verification-derived loss and data augmentation for DNN-based multispeaker speech synthesis—0
Data augmentation and pre-trained networks for extremely low data regimes unsupervised visual inspection—0
Long Term Object Detection and Tracking in Collaborative Learning Environments—0
Benchmarking CNN on 3D Anatomical Brain MRI: Architectures, Data Augmentation and Deep Ensemble Learning—0
CoRI: Collective Relation Integration with Data Augmentation for Open Information Extraction—0
Automatic Classification of Attributes in German Adjective-Noun PhrasesCode0
Target-Aware Data Augmentation for Stance Detection—0
Generalization in Instruction Following Systems—0
Multilingual Speech Translation with Unified Transformer: Huawei Noah's Ark Lab at IWSLT 2021—0
Training Language Models under Resource Constraints for Adversarial Advertisement Detection—0
NAMER: A Node-Based Multitasking Framework for Multi-Hop Knowledge Base Question AnsweringCode0
Training Data Augmentation for Code-Mixed TranslationCode0
IIITN NLP at SMM4H 2021 Tasks: Transformer Models for Classification on Health-Related Imbalanced Twitter Datasets—0
Combining Weakly Supervised ML Techniques for Low-Resource NLU—0
How low is too low? A monolingual take on lemmatisation in Indian languages—0
Adversarial VQA: A New Benchmark for Evaluating the Robustness of VQA Models—0
DEFT 2021: Évaluation automatique de réponses courtes, une approche basée sur la sélection de traits lexicaux et augmentation de données (DEFT 2021 : Automatic short answer grading, a lexical features selection and data augmentation based approach)Code0
UoB at ProfNER 2021: Data Augmentation for Classification Using Machine Translation—0
Raman spectral analysis of mixtures with one-dimensional convolutional neural network—0
TopGuNN: Fast NLP Training Data Augmentation using Large CorporaCode0
Concurrent Adversarial Learning for Large-Batch Training—0
Practical Transformer-based Multilingual Text ClassificationCode0
An ordinal CNN approach for the assessment of neurological damage in Parkinson's disease patientsCode0
Closer Look at the Uncertainty Estimation in Semantic Segmentation under Distributional Shift—0
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All datasetsImageNetCIFAR-10GA1457

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1DeiT-B (+MixPro)Accuracy (%)82.9—Unverified
2ResNet-200 (DeepAA)Accuracy (%)81.32—Unverified
3DeiT-S (+MixPro)Accuracy (%)81.3—Unverified
4ResNet-200 (Fast AA)Accuracy (%)80.6—Unverified
5ResNet-200 (UA)Accuracy (%)80.4—Unverified
6ResNet-200 (AA)Accuracy (%)80—Unverified
7ResNet-50 (DeepAA)Accuracy (%)78.3—Unverified
8ResNet-50 (TA wide)Accuracy (%)78.07—Unverified
9ResNet-50 (LoRot-E)Accuracy (%)77.72—Unverified
10ResNet-50 (LoRot-I)Accuracy (%)77.71—Unverified
#ModelMetricClaimedVerifiedStatus
1WideResNet-40-2 (Faster AA)Percentage error3.7—Unverified
2Shake-Shake (26 2×32d) (Faster AA)Percentage error2.7—Unverified
3WideResNet-28-10 (Faster AA)Percentage error2.6—Unverified
4Shake-Shake (26 2×112d) (Faster AA)Percentage error2—Unverified
5Shake-Shake (26 2×96d) (Faster AA)Percentage error2—Unverified
#ModelMetricClaimedVerifiedStatus
1DiffAugClassification Accuracy92.7—Unverified
2PaCMAPClassification Accuracy85.3—Unverified
3hNNEClassification Accuracy77.4—Unverified
4TopoAEClassification Accuracy74.6—Unverified